dialogueclassification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "045b4e09-52d2-403e-806b-9198ff2e5127",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "#https://huggingface.co/diwank/silicone-deberta-pair\n",
    "model = \"diwank/silicone-deberta-pair\"\n",
    "tokenizer = \"diwank/silicone-deberta-pair\"\n",
    "pip = pipeline('text-classification', model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "15598915-1e32-412f-957d-4d017a43c700",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (0, 'acknowledge')\n",
    "# (1, 'answer')\n",
    "# (2, 'backchannel')\n",
    "# (3, 'reply_yes')\n",
    "# (4, 'exclaim')\n",
    "# (5, 'say')\n",
    "# (6, 'reply_no')\n",
    "# (7, 'hold')\n",
    "# (8, 'ask')\n",
    "# (9, 'intent')\n",
    "# (10, 'ask_yes_no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa3daca8-3dc3-4e04-8869-5564737522f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = [\"I love cats\", \"Do you love cats?\", \"Yes, I do\", \"No, dogs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1ea7d41-ebbb-4005-8c2c-5244fb178af6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'label': 'LABEL_5', 'score': 0.7644516229629517}, {'label': 'LABEL_8', 'score': 0.9776815176010132}, {'label': 'LABEL_3', 'score': 0.5020651817321777}, {'label': 'LABEL_5', 'score': 0.5964227318763733}]\n"
     ]
    }
   ],
   "source": [
    "response = pip(sentences)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dbcd00e-8e92-445b-b02a-1a7197d954cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "364a7613-64e4-4b6f-a559-de15bbf315ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import RobertaTokenizer, RobertaForSequenceClassification\n",
    "\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "773ec028-2646-459d-add1-dbd71d3dc2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DialogTag:\n",
    "    def __init__(self, num_labels=23):\n",
    "        # Set up GPU if available\n",
    "        self._device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "        self._tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
    "        self._model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=num_labels)\n",
    "        self._model.to(self._device)\n",
    "\n",
    "        self._label2id = dict()\n",
    "        self._id2label = dict()\n",
    "\n",
    "    def _tokenize(self, strings):\n",
    "        return self._tokenizer(strings, padding=True, return_tensors='pt').to(self._device)\n",
    "\n",
    "    def _encode_labels(self, labels):\n",
    "        int_labels = []\n",
    "        for label in labels:\n",
    "            if label not in self._label2id:\n",
    "                self._label2id[label] = len(self._label2id)\n",
    "                self._id2label[len(self._id2label)] = label\n",
    "            int_labels.append(self._label2id[label])\n",
    "        return torch.LongTensor(int_labels).to(self._device)\n",
    "\n",
    "    def fit(self, data, epochs=4, batch_size=32, lrate=1e-5):\n",
    "        # Preprocess turns and index labels\n",
    "        strings = [t0 + self._tokenizer.sep_token + t1 for t0, t1, _ in data]\n",
    "        labels = [l for _, _, l in data]\n",
    "\n",
    "        X = [self._tokenize(strings[i:i + batch_size]) for i in range(0, len(strings), batch_size)]\n",
    "        y = [self._encode_labels(labels[i:i + batch_size]) for i in range(0, len(labels), batch_size)]\n",
    "\n",
    "        # Setup optimizer and objective function\n",
    "        optimizer = torch.optim.Adam(self._model.parameters(), lr=lrate)\n",
    "        criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "            losses = []\n",
    "\n",
    "            for X_batch, y_batch in tqdm(zip(X, y)):\n",
    "                y_pred = self._model(**X_batch)\n",
    "                loss = criterion(y_pred.logits, y_batch)\n",
    "                losses.append(loss.item())\n",
    "\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "            print(np.mean(losses))\n",
    "\n",
    "    def predict(self, turn0, turn1):\n",
    "        string = turn0 + self._tokenizer.sep_token + turn1\n",
    "        X = self._tokenize([string])\n",
    "        y = self._model(**X).logits.cpu().detach().numpy()\n",
    "        return self._id2label[np.argmax(y[0])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b71e25-6a29-4ce2-984c-33169ce6b134",
   "metadata": {},
   "source": [
    "## MIDAS Dialogue Act"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6fa2f25-e38d-4fa1-b3bc-fbf41182cae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pytorch-pretrained-bert\n",
      "  Using cached pytorch_pretrained_bert-0.6.2-py3-none-any.whl (123 kB)\n",
      "Requirement already satisfied: numpy in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from pytorch-pretrained-bert) (1.22.0)\n",
      "Requirement already satisfied: requests in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from pytorch-pretrained-bert) (2.27.1)\n",
      "Requirement already satisfied: regex in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from pytorch-pretrained-bert) (2021.11.2)\n",
      "Requirement already satisfied: tqdm in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from pytorch-pretrained-bert) (4.62.3)\n",
      "Requirement already satisfied: torch>=0.4.1 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from pytorch-pretrained-bert) (1.10.2)\n",
      "Requirement already satisfied: boto3 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from pytorch-pretrained-bert) (1.21.42)\n",
      "Requirement already satisfied: typing_extensions in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from torch>=0.4.1->pytorch-pretrained-bert) (3.10.0.2)\n",
      "Requirement already satisfied: botocore<1.25.0,>=1.24.42 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from boto3->pytorch-pretrained-bert) (1.24.42)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from boto3->pytorch-pretrained-bert) (1.0.0)\n",
      "Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from boto3->pytorch-pretrained-bert) (0.5.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from requests->pytorch-pretrained-bert) (1.26.12)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from requests->pytorch-pretrained-bert) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from requests->pytorch-pretrained-bert) (2022.9.14)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from requests->pytorch-pretrained-bert) (2.0.4)\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from botocore<1.25.0,>=1.24.42->boto3->pytorch-pretrained-bert) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/piek/opt/anaconda3/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.25.0,>=1.24.42->boto3->pytorch-pretrained-bert) (1.16.0)\n",
      "Installing collected packages: pytorch-pretrained-bert\n",
      "Successfully installed pytorch-pretrained-bert-0.6.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pytorch-pretrained-bert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "672ba723-9123-44ac-bd6b-dc9f1a4e6e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig, WEIGHTS_NAME, CONFIG_NAME\n",
    "from pytorch_pretrained_bert.tokenization import BertTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc63208d-2176-4c35-b49b-440bb8544248",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://github.com/DianDYu/MIDAS_dialog_act\n",
    "# 23 labels\n",
    "\n",
    "NOTE_DA_MAP = {\"sd\": \"statement\", \n",
    "               \"b\": \"back-channeling\", \n",
    "               \"sv\": \"opinion\", \n",
    "               \"ny\": \"pos_answer\", \n",
    "               \"%\": \"abandon\",\n",
    "               \"ba\": \"appreciation\",\n",
    "               \"qy\": \"yes_no_question\", \n",
    "               \"fc\": \"closing\", \n",
    "               \"ng\": \"neg_answer\", \n",
    "               \"h\": \"other_answers\", \n",
    "               \"o\": \"other\",\n",
    "               \"sv\": \"opinion\", \n",
    "               \"ad\": \"command\", \n",
    "               \"^h\": \"hold\", \n",
    "               \"cp\": \"complaint\", \n",
    "               \"fp\": \"opening\",\n",
    "               \"bd\": \"respond_to_apology\",\n",
    "               \"fa\": \"apology\", \n",
    "               \"ft\": \"thanking\",\n",
    "               \"oqf\": \"open_question_factual\", \n",
    "               \"oqo\": \"open_question_opinion\", \n",
    "               \"cm\": \"comment\",\n",
    "               \"ns\": \"nonsense\", \n",
    "               \"dad\": \"dev_command\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6f85b7f0-c615-4ffc-80ce-663f35ecd087",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 56%|████████████████████████████████████████████████████████████████████████████▉                                                             | 227534848/407873900 [01:00<00:39, 4515960.39B/s]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/6w/bw7dqbl9727c48pcjjh32r140000gn/T/ipykernel_78645/924338803.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBertTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bert-base-uncased\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_lower_case\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"src/cltl/models/midas-da-bert\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n\u001b[0m\u001b[1;32m      4\u001b[0m               \u001b[0mcache_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m               num_labels = 23)\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pytorch_pretrained_bert/modeling.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    564\u001b[0m         \u001b[0;31m# redirect to the cache, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0mresolved_archive_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcached_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marchive_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m             logger.error(\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pytorch_pretrained_bert/file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[0;34m(url_or_filename, cache_dir)\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mparsed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscheme\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'http'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'https'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0;31m# URL, so get it from the cache (downloading if necessary)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mget_from_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;31m# File, and it exists.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pytorch_pretrained_bert/file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[0;34m(url, cache_dir)\u001b[0m\n\u001b[1;32m    228\u001b[0m                 \u001b[0ms3_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m                 \u001b[0mhttp_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0;31m# we are copying the file before closing it, so flush to avoid truncation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pytorch_pretrained_bert/file_utils.py\u001b[0m in \u001b[0;36mhttp_get\u001b[0;34m(url, temp_file)\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent_length\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcontent_length\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0mprogress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"B\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_content\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1024\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# filter out keep-alive new chunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m             \u001b[0mprogress\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/requests/models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'stream'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 760\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    761\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    762\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mProtocolError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    625\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_fp_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_error_catcher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfp_closed\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34mb\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mamt\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m                 \u001b[0mflush_decoder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36m_fp_read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    530\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m             \u001b[0;31m# StringIO doesn't like amt=None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mamt\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    460\u001b[0m             \u001b[0;31m# Amount is given, implement using readinto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m             \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmemoryview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0;31m# connection, and the user is reading more bytes than will be provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0;31m# (for example, reading in 1k chunks)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    507\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m             \u001b[0;31m# Ideally, we would raise IncompleteRead if the content-length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    702\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1239\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1240\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1241\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1242\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1243\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1097\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1098\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1100\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\", do_lower_case=True)\n",
    "model_path = \"src/cltl/models/midas-da-bert\"\n",
    "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n",
    "              cache_dir=model_path,\n",
    "              num_labels = 23)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e44a3b61-f3f0-4769-9549-a3e83aeb21a9",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not load model src/cltl/models/midas-da-bert with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForSequenceClassification'>, <class 'transformers.models.auto.modeling_tf_auto.TFAutoModelForSequenceClassification'>).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/6w/bw7dqbl9727c48pcjjh32r140000gn/T/ipykernel_78645/1510892457.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"src/cltl/models/midas-da-bert\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpip_midas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'text-classification'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, framework, revision, use_fast, use_auth_token, model_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    460\u001b[0m     \u001b[0;31m# Will load the correct model if possible\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0mmodel_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"tf\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tf\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m     framework, model = infer_framework_load_model(\n\u001b[0m\u001b[1;32m    463\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m         \u001b[0mmodel_classes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_classes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36minfer_framework_load_model\u001b[0;34m(model, config, model_classes, task, framework, **model_kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Could not load model {model} with any of the following classes: {class_tuple}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0mframework\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"tf\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"TF\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Could not load model src/cltl/models/midas-da-bert with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForSequenceClassification'>, <class 'transformers.models.auto.modeling_tf_auto.TFAutoModelForSequenceClassification'>)."
     ]
    }
   ],
   "source": [
    "model_path = \"src/cltl/models/midas-da-bert\"\n",
    "pip_midas = pipeline('text-classification', model=model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01296f58-6ec4-493c-ba4d-e904babd0d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "python run_classifier.py --data_dir da_data/ --bert_model output --task_name da --output_dir output --do_eval --binary_pred"
   ]
  }
 ],
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